"""

md_tests.py

A series of tests to validate basic repo functionality and verify either "correct"
inference behavior, or - when operating in environments other than the training
environment - acceptable deviation from the correct results.

This module should not depend on anything else in this repo outside of the 
tests themselves, even if it means some duplicated code (e.g. for downloading files),
since much of what it tries to test is, e.g., imports.

"Correctness" is determined by agreement with a file that this script fetches from lila.science.

"""

#%% Imports and constants

### Only standard imports belong here, not MD-specific imports ###

import os
import json
import glob
import tempfile
import urllib
import urllib.request
import zipfile
import subprocess
import argparse
import inspect

#: IoU threshold used to determine whether boxes in two detection files likely correspond
#: to the same box.
iou_threshold_for_file_comparison = 0.9


#%% Classes

class MDTestOptions:
    """
    Options controlling test behavior
    """
    
    def __init__(self):
    
        ## Required ##
    
        #: Force CPU execution
        self.disable_gpu = False
        
        #: If GPU execution is requested, but a GPU is not available, should we error?
        self.cpu_execution_is_error = False
        
        #: Skip tests related to video processing
        self.skip_video_tests = False
        
        #: Skip tests launched via Python functions (as opposed to CLIs)
        self.skip_python_tests = False
        
        #: Skip CLI tests
        self.skip_cli_tests = False
        
        #: Force a specific folder for temporary input/output
        self.scratch_dir = None
        
        #: Where does the test data live?
        self.test_data_url = 'https://lila.science/public/md-test-package.zip'
        
        #: Download test data even if it appears to have already been downloaded
        self.force_data_download = False
        
        #: Unzip test data even if it appears to have already been unzipped
        self.force_data_unzip = False
        
        #: By default, any unexpected behavior is an error; this forces most errors to
        #: be treated as warnings.
        self.warning_mode = False
        
        #: How much deviation from the expected detection coordinates should we allow before
        #: a disrepancy becomes an error?
        self.max_coord_error = 0.001
        
        #: How much deviation from the expected confidence values should we allow before
        #: a disrepancy becomes an error?
        self.max_conf_error = 0.005
        
        #: Current working directory when running CLI tests
        #:
        #: If this is None, we won't mess with the inherited working directory.
        self.cli_working_dir = None
        
        #: YOLOv5 installation, only relevant if we're testing run_inference_with_yolov5_val. 
        #:
        #: If this is None, we'll skip that test.
        self.yolo_working_dir = None
        
        #: fourcc code to use for video tests that involve rendering video
        self.video_fourcc = 'mp4v'   
        
        #: Default model to use for testing (filename, URL, or well-known model string)
        self.default_model = 'MDV5A'

        #: For comparison tests, use a model that produces slightly different output
        self.alt_model = 'MDV5B'
        
        #: PYTHONPATH to set for CLI tests; if None, inherits from the parent process.  Only
        #: impacts the called functions, not the parent process.
        self.cli_test_pythonpath = None

# ...class MDTestOptions()


#%% Support functions

def get_expected_results_filename(gpu_is_available,
                                  model_string='mdv5a',
                                  test_type='image',
                                  augment=False,
                                  options=None):
    """
    Expected results vary just a little across inference environments, particularly
    between PT 1.x and 2.x, so when making sure things are working acceptably, we 
    compare to a reference file that matches the current environment.
    
    This function gets the correct filename to compare to current results, depending
    on whether a GPU is available.
    
    Args:
        gpu_is_available (bool): whether a GPU is available
        
    Returns:
        str: relative filename of the results file we should use (within the test
        data zipfile)
    """
    
    if gpu_is_available:
        hw_string = 'gpu'
    else:
        hw_string = 'cpu'
    import torch
    torch_version = str(torch.__version__)
    if torch_version.startswith('1'):
        assert torch_version == '1.10.1', 'Only tested against PT 1.10.1 and PT 2.x'
        pt_string = 'pt1.10.1'
    else:
        assert torch_version.startswith('2'), 'Unknown torch version: {}'.format(torch_version)
        pt_string = 'pt2.x'
    
    # A hack for now to account for the fact that even with acceleration enabled and PT2 
    # installed, Apple silicon appears to provide the same results as CPU/PT1 inference
    try:
        import torch
        m1_inference = torch.backends.mps.is_built and torch.backends.mps.is_available()
        if m1_inference:
            print('I appear to be running on M1/M2 hardware, using pt1/cpu as the reference results')
            hw_string = 'cpu'
            pt_string = 'pt1.10.1'
    except Exception:
        pass
    
    aug_string = ''
    if augment:
        aug_string = 'augment-'
        
    fn = '{}-{}{}-{}-{}.json'.format(model_string,aug_string,test_type,hw_string,pt_string)
    
    from megadetector.utils.path_utils import insert_before_extension
    
    if test_type == 'video':
        fn = insert_before_extension(fn,'frames')
    
    if options is not None and options.scratch_dir is not None:
        fn = os.path.join(options.scratch_dir,fn)
        
    return fn
    
    
def download_test_data(options=None):
    """
    Downloads the test zipfile if necessary, unzips if necessary.
    
    Args:
        options (MDTestOptions, optional): see MDTestOptions for details
        
    Returns:
        MDTestOptions: the same object passed in as input, or the options that
        were used if [options] was supplied as None
    """

    if options is None:
        options = MDTestOptions()
        
    if options.scratch_dir is None:        
        tempdir_base = tempfile.gettempdir()
        scratch_dir = os.path.join(tempdir_base,'md-tests')
    else:
        scratch_dir = options.scratch_dir
    
    os.makedirs(scratch_dir,exist_ok=True)    
    
    # See whether we've already downloaded the data zipfile
    download_zipfile = True        
    if not options.force_data_download:
        local_zipfile = os.path.join(scratch_dir,options.test_data_url.split('/')[-1])
        if os.path.isfile(local_zipfile):
            url_info = urllib.request.urlopen(options.test_data_url).info()
            remote_size = int(url_info['Content-Length'])
            target_file_size = os.path.getsize(local_zipfile)
            if remote_size == target_file_size:
                download_zipfile = False
    
    if download_zipfile:
        print('Downloading test data zipfile')
        urllib.request.urlretrieve(options.test_data_url, local_zipfile)
        print('Finished download to {}'.format(local_zipfile))
    else:
        print('Bypassing test data zipfile download for {}'.format(local_zipfile))
    
    
    ## Unzip data
    
    zipf = zipfile.ZipFile(local_zipfile)    
    zip_contents = zipf.filelist
    
    # file_info = zip_contents[1]
    for file_info in zip_contents:
        
        expected_size = file_info.file_size
        if expected_size == 0:
            continue
        fn_relative = file_info.filename
        target_file = os.path.join(scratch_dir,fn_relative)
        unzip_file = True
        if (not options.force_data_unzip) and os.path.isfile(target_file):
            existing_file_size = os.path.getsize(target_file)
            if existing_file_size == expected_size:
                unzip_file = False
        if unzip_file:
            os.makedirs(os.path.dirname(target_file),exist_ok=True)
            with open(target_file,'wb') as f:
                f.write(zipf.read(fn_relative))
            
    # ...for each file in the zipfile
    
    try:
        zipf.close()
    except Exception as e:
        print('Warning: error closing zipfile:\n{}'.format(str(e)))
        
    # Warn if files are present that aren't expected
    test_files = glob.glob(os.path.join(scratch_dir,'**/*'), recursive=True)
    test_files = [os.path.relpath(fn,scratch_dir).replace('\\','/') for fn in test_files]
    test_files_set = set(test_files)
    expected_images_set = set(zipf.namelist())
    for fn in expected_images_set:
        if fn.endswith('/'):
            continue
        assert fn in test_files_set, 'File {} is missing from the test image folder'.format(fn)
    
    # Populate the test options with test data information
    options.scratch_dir = scratch_dir
    options.all_test_files = test_files
    options.test_images = [fn for fn in test_files if os.path.splitext(fn.lower())[1] in ('.jpg','.jpeg','.png')]
    options.test_videos = [fn for fn in test_files if os.path.splitext(fn.lower())[1] in ('.mp4','.avi')]    
    options.test_videos = [fn for fn in options.test_videos if 'rendered' not in fn]
    options.test_videos = [fn for fn in options.test_videos if \
                           os.path.isfile(os.path.join(scratch_dir,fn))]
        
    print('Finished unzipping and enumerating test data')
    
    return options

# ...def download_test_data(...)


def is_gpu_available(verbose=True):
    """
    Checks whether a GPU (including M1/M2 MPS) is available.
    
    Args:
        verbose (bool, optional): enable additional debug console output
    
    Returns:
        bool: whether a GPU is available
    """
    
    # Import torch inside this function, so we have a chance to set CUDA_VISIBLE_DEVICES
    # before checking GPU availability.
    import torch
    gpu_available = torch.cuda.is_available()
    
    if gpu_available:
        if verbose:
            print('CUDA available: {}'.format(gpu_available))
            device_ids = list(range(torch.cuda.device_count()))
            if len(device_ids) > 1:
                print('Found multiple devices: {}'.format(str(device_ids)))
    else:
        try:
            gpu_available = torch.backends.mps.is_built and torch.backends.mps.is_available()
        except AttributeError:
            pass
        if gpu_available:
            print('Metal performance shaders available')
    
    if not gpu_available:
        print('No GPU available')
        
    return gpu_available            

# ...def is_gpu_available(...)     


def output_files_are_identical(fn1,fn2,verbose=False):
    """
    Checks whether two MD-formatted output files are identical other than file sorting.
    
    Args:
        fn1 (str): the first filename to compare
        fn2 (str): the second filename to compare
    
    Returns:
        bool: whether [fn1] and [fn2] are identical other than file sorting.
    """
    
    if verbose:
        print('Comparing {} to {}'.format(fn1,fn2))
    
    with open(fn1,'r') as f:
         fn1_results = json.load(f)
    fn1_results['images'] = \
         sorted(fn1_results['images'], key=lambda d: d['file'])
    
    with open(fn2,'r') as f:
         fn2_results = json.load(f)
    fn2_results['images'] = \
         sorted(fn2_results['images'], key=lambda d: d['file'])
     
    if len(fn1_results['images']) != len(fn1_results['images']):
        if verbose:
            print('{} images in {}, {} images in {}'.format(
                len(fn1_results['images']),fn1,
                len(fn2_results['images']),fn2))
        return False
    
    # i_image = 0; fn1_image = fn1_results['images'][i_image]
    for i_image,fn1_image in enumerate(fn1_results['images']):
        
        fn2_image = fn2_results['images'][i_image]
        
        if fn1_image['file'] != fn2_image['file']:
            if verbose:
                print('Filename difference at {}: {} vs {} '.format(i_image,fn1_image['file'],fn1_image['file']))
            return False
            
        if fn1_image != fn2_image:
            if verbose:
                print('Image-level difference in image {}: {}'.format(i_image,fn1_image['file']))
            return False
        
    return True

# ...def output_files_are_identical(...)
   

def compare_detection_lists(detections_a,detections_b,options,bidirectional_comparison=True):
    """
    Compare two lists of MD-formatted detections, matching detections across lists using IoU
    criteria.  Generally used to compare detections for the same image when two sets of results 
    are expected to be more or less the same.
    
    Args:
        detections_a (list): the first set of detection dicts
        detections_b (list): the second set of detection dicts
        options (MDTestOptions): options that determine tolerable differences between files    
        bidirectional_comparison (bool, optional): reverse the arguments and make a recursive
            call.
            
    Returns:
        dict: a dictionary with keys 'max_conf_error' and 'max_coord_error'.
    """
    from megadetector.utils.ct_utils import get_iou
    
    max_conf_error = 0
    max_coord_error = 0
        
    # i_det_a = 0
    for i_det_a in range(0,len(detections_a)):
        
        det_a = detections_a[i_det_a]
        
        # Don't process very-low-confidence boxes
        if det_a['conf'] < options.max_conf_error:
            continue
        
        matching_det_b = None
        highest_iou = -1
        
        # Find the closest match in the detections_b list
        
        # i_det_b = 0
        for i_det_b in range(0,len(detections_b)):
            
            b_det = detections_b[i_det_b]
            
            if b_det['category'] != det_a['category']:
                continue
            
            iou = get_iou(det_a['bbox'],b_det['bbox'])
            
            # Is this likely the same detection as det_a?
            if iou >= iou_threshold_for_file_comparison and iou > highest_iou:
                matching_det_b = b_det
                highest_iou = iou
                
        # If there are no detections in this category in detections_b
        if matching_det_b is None:
            if det_a['conf'] > max_conf_error:
                max_conf_error = det_a['conf']
            # max_coord_error = 1.0
            continue
        
        assert det_a['category'] == matching_det_b['category']
        conf_err = abs(det_a['conf'] - matching_det_b['conf'])
        coord_differences = []
        for i_coord in range(0,4):
            coord_differences.append(abs(det_a['bbox'][i_coord]-\
                                         matching_det_b['bbox'][i_coord]))
        coord_err = max(coord_differences)
        
        if conf_err >= max_conf_error:
            max_conf_error = conf_err
            
        if coord_err >= max_coord_error:
            max_coord_error = coord_err            
    
    # ...for each detection in detections_a
    
    if bidirectional_comparison:
        
        reverse_comparison_results = compare_detection_lists(detections_b,
                                                             detections_a, 
                                                             options, 
                                                             bidirectional_comparison=False)
        
        if reverse_comparison_results['max_conf_error'] > max_conf_error:
            max_conf_error = reverse_comparison_results['max_conf_error']
        if reverse_comparison_results['max_coord_error'] > max_coord_error:
            max_coord_error = reverse_comparison_results['max_coord_error']
    
    list_comparison_results = {}
    list_comparison_results['max_coord_error'] = max_coord_error
    list_comparison_results['max_conf_error'] = max_conf_error
    
    return list_comparison_results

# ...def compare_detection_lists(...)


def compare_results(inference_output_file,expected_results_file,options):
    """
    Compare two MD-formatted output files that should be nearly identical, allowing small
    changes (e.g. rounding differences).  Generally used to compare a new results file to 
    an expected results file.
    
    Args:
        inference_output_file (str): the first results file to compare
        expected_results_file (str): the second results file to compare
        options (MDTestOptions): options that determine tolerable differences between files
        
    Returns:
        dict: dictionary with keys 'max_coord_error' and 'max_conf_error'
    """
    
    # Read results
    with open(inference_output_file,'r') as f:
        results_from_file = json.load(f) # noqa
    
    with open(os.path.join(options.scratch_dir,expected_results_file),'r') as f:
        expected_results = json.load(f)
            
    filename_to_results = {im['file'].replace('\\','/'):im for im in results_from_file['images']}
    filename_to_results_expected = {im['file'].replace('\\','/'):im for im in expected_results['images']}
    
    assert len(filename_to_results) == len(filename_to_results_expected), \
        'Error: expected {} files in results, found {}'.format(
            len(filename_to_results_expected),
            len(filename_to_results))
    
    max_conf_error = 0
    max_conf_error_file = None
    
    max_coord_error = 0
    max_coord_error_file = None    
    
    # fn = next(iter(filename_to_results.keys()))
    for fn in filename_to_results.keys():
                
        actual_image_results = filename_to_results[fn]
        expected_image_results = filename_to_results_expected[fn]
        
        if 'failure' in actual_image_results:
            assert 'failure' in expected_image_results and \
                'detections' not in actual_image_results and \
                'detections' not in expected_image_results
            continue
        assert 'failure' not in expected_image_results
        
        actual_detections = actual_image_results['detections']
        expected_detections = expected_image_results['detections']
        
        comparison_results_this_image = compare_detection_lists(
            detections_a=actual_detections,
            detections_b=expected_detections,
            options=options,
            bidirectional_comparison=True)
        
        if comparison_results_this_image['max_conf_error'] > max_conf_error:
            max_conf_error = comparison_results_this_image['max_conf_error']
            max_conf_error_file = fn
            
        if comparison_results_this_image['max_coord_error'] > max_coord_error:
            max_coord_error = comparison_results_this_image['max_coord_error']
            max_coord_error_file = fn
                
    # ...for each image
    
    if not options.warning_mode:
        
        assert max_conf_error <= options.max_conf_error, \
            'Confidence error {} is greater than allowable ({})'.format(
                max_conf_error,options.max_conf_error)
        
        assert max_coord_error <= options.max_coord_error, \
            'Coord error {} is greater than allowable ({})'.format(
                max_coord_error,options.max_coord_error)
        
    print('Max conf error: {} (file {})'.format(
        max_conf_error,max_conf_error_file))
    print('Max coord error: {} (file {})'.format(
        max_coord_error,max_coord_error_file))
    
    comparison_results = {}
    comparison_results['max_conf_error'] = max_conf_error
    comparison_results['max_coord_error'] = max_coord_error

    return comparison_results

# ...def compare_results(...)


def _args_to_object(args, obj):
    """
    Copies all fields from a Namespace (typically the output from parse_args) to an
    object. Skips fields starting with _. Does not check existence in the target
    object.

    Args:
        args (argparse.Namespace): the namespace to convert to an object
        obj (object): object whose whose attributes will be updated
        
    Returns:
        object: the modified object (modified in place, but also returned)
    """
    
    for n, v in inspect.getmembers(args):
        if not n.startswith('_'):
            setattr(obj, n, v)

    return obj

    
#%% CLI functions

# These are copied from process_utils.py to avoid imports outside of the test
# functions.

os.environ["PYTHONUNBUFFERED"] = "1"

def execute(cmd):
    """
    Runs [cmd] (a single string) in a shell, yielding each line of output to the caller.
    
    Args:
        cmd (str): command to run
    
    Returns:
        int: the command's return code, always zero, otherwise a CalledProcessError is raised
    """
 
    # https://stackoverflow.com/questions/4417546/constantly-print-subprocess-output-while-process-is-running
    popen = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
                             shell=True, universal_newlines=True)
    for stdout_line in iter(popen.stdout.readline, ""):
        yield stdout_line
    popen.stdout.close()
    return_code = popen.wait()
    if return_code:
        raise subprocess.CalledProcessError(return_code, cmd)
    return return_code


def execute_and_print(cmd,print_output=True,catch_exceptions=False,echo_command=True):
    """
    Runs [cmd] (a single string) in a shell, capturing (and optionally printing) output.
    
    Args:
        cmd (str): command to run
        print_output (bool, optional): whether to print output from [cmd]
    
    Returns:
        dict: a dictionary with fields "status" (the process return code) and "output"
        (the content of stdout)
    """

    if echo_command:
        print('Running command:\n{}\n'.format(cmd))
        
    to_return = {'status':'unknown','output':''}
    output = []
    try:
        for s in execute(cmd):
            output.append(s)
            if print_output:
                print(s,end='',flush=True)
        to_return['status'] = 0
    except subprocess.CalledProcessError as cpe:
        if not catch_exceptions:
            raise
        print('execute_and_print caught error: {}'.format(cpe.output))
        to_return['status'] = cpe.returncode
    to_return['output'] = output
   
    return to_return


#%% Python tests

def run_python_tests(options):
    """
    Runs Python-based (as opposed to CLI-based) package tests.
    
    Args:
        options (MDTestOptions): see MDTestOptions for details
    """
    
    print('\n*** Starting module tests ***\n')
    
    ## Prepare data
    
    download_test_data(options)
    
    
    ## Run inference on an image
    
    print('\n** Running MD on a single image **\n')
    
    from megadetector.detection import run_detector
    from megadetector.visualization import visualization_utils as vis_utils
    image_fn = os.path.join(options.scratch_dir,options.test_images[0])
    model = run_detector.load_detector(options.default_model)
    pil_im = vis_utils.load_image(image_fn)
    result = model.generate_detections_one_image(pil_im) # noqa

    
    ## Run inference on a folder

    print('\n** Running MD on a folder of images **\n')
    
    from megadetector.detection.run_detector_batch import load_and_run_detector_batch,write_results_to_file
    from megadetector.utils import path_utils

    image_folder = os.path.join(options.scratch_dir,'md-test-images')
    assert os.path.isdir(image_folder), 'Test image folder {} is not available'.format(image_folder)
    inference_output_file = os.path.join(options.scratch_dir,'folder_inference_output.json')
    image_file_names = path_utils.find_images(image_folder,recursive=True)
    results = load_and_run_detector_batch(options.default_model, image_file_names, quiet=True)
    _ = write_results_to_file(results,
                              inference_output_file,
                              relative_path_base=image_folder,
                              detector_file=options.default_model)

    
    ## Verify results
    
    expected_results_file = get_expected_results_filename(is_gpu_available(verbose=False),
                                                          options=options)
    compare_results(inference_output_file,expected_results_file,options)
        
    # Make note of this filename, we will use it again later
    inference_output_file_standard_inference = inference_output_file
    
    
    ## Run and verify again with augmentation enabled
    
    print('\n** Running MD on images with augmentation **\n')
    
    from megadetector.utils.path_utils import insert_before_extension
    
    inference_output_file_augmented = insert_before_extension(inference_output_file,'augmented')
    results = load_and_run_detector_batch(options.default_model, image_file_names, quiet=True, augment=True)
    _ = write_results_to_file(results,
                              inference_output_file_augmented,
                              relative_path_base=image_folder,
                              detector_file=options.default_model)

    expected_results_file_augmented = \
        get_expected_results_filename(is_gpu_available(verbose=False),
                                      augment=True,options=options)
    compare_results(inference_output_file_augmented,expected_results_file_augmented,options)
        
    
    ## Postprocess results
    
    from megadetector.postprocessing.postprocess_batch_results import \
        PostProcessingOptions,process_batch_results
    postprocessing_options = PostProcessingOptions()
    
    postprocessing_options.md_results_file = inference_output_file
    postprocessing_options.output_dir = os.path.join(options.scratch_dir,'postprocessing_output')
    postprocessing_options.image_base_dir = image_folder
    
    postprocessing_results = process_batch_results(postprocessing_options)
    assert os.path.isfile(postprocessing_results.output_html_file), \
        'Postprocessing output file {} not found'.format(postprocessing_results.output_html_file)
    
        
    ## Partial RDE test
    
    from megadetector.postprocessing.repeat_detection_elimination.repeat_detections_core import \
        RepeatDetectionOptions, find_repeat_detections
    
    rde_options = RepeatDetectionOptions()
    rde_options.occurrenceThreshold = 2
    rde_options.confidenceMin = 0.001
    rde_options.outputBase = os.path.join(options.scratch_dir,'rde_working_dir')
    rde_options.imageBase = image_folder
    rde_output_file = inference_output_file.replace('.json','_filtered.json')
    assert rde_output_file != inference_output_file
    rde_results = find_repeat_detections(inference_output_file, rde_output_file, rde_options)
    assert os.path.isfile(rde_results.filterFile),\
        'Could not find RDE output file {}'.format(rde_results.filterFile)
        
    
    ## Run inference on a folder (with YOLOv5 val script)
    
    if options.yolo_working_dir is None:
        
        print('Skipping YOLO val inference tests, no YOLO folder supplied')
        
    else:
            
        from megadetector.detection.run_inference_with_yolov5_val import \
            YoloInferenceOptions, run_inference_with_yolo_val
        from megadetector.utils.path_utils import insert_before_extension
        
        inference_output_file_yolo_val = os.path.join(options.scratch_dir,'folder_inference_output_yolo_val.json')
        
        yolo_inference_options = YoloInferenceOptions()
        yolo_inference_options.input_folder = os.path.join(options.scratch_dir,'md-test-images')
        yolo_inference_options.output_file = inference_output_file_yolo_val
        yolo_inference_options.yolo_working_folder = options.yolo_working_dir
        yolo_inference_options.model_filename = options.default_model
        yolo_inference_options.augment = False
        yolo_inference_options.overwrite_handling = 'overwrite'
        from megadetector.detection.run_detector import DEFAULT_OUTPUT_CONFIDENCE_THRESHOLD
        yolo_inference_options.conf_thres = DEFAULT_OUTPUT_CONFIDENCE_THRESHOLD
        
        run_inference_with_yolo_val(yolo_inference_options)
        
        ## Confirm this matches the standard inference path
        
        if False:
            # TODO: compare_resluts() isn't quite ready for this yet
            compare_results(inference_output_file=inference_output_file_yolo_val, 
                            expected_results_file=inference_output_file_standard_inference, 
                            options=options)
        
        
        
        # Run again, without symlinks this time
        
        inference_output_file_yolo_val_no_links = insert_before_extension(inference_output_file_yolo_val,
                                                                          'no-links')
        yolo_inference_options.output_file = inference_output_file_yolo_val_no_links
        yolo_inference_options.use_symlinks = False
        run_inference_with_yolo_val(yolo_inference_options)
    
        # Run again, with chunked inference and symlinks
        
        inference_output_file_yolo_val_checkpoints = insert_before_extension(inference_output_file_yolo_val,
                                                                          'checkpoints')
        yolo_inference_options.output_file = inference_output_file_yolo_val_checkpoints
        yolo_inference_options.use_symlinks = True
        yolo_inference_options.checkpoint_frequency = 5
        run_inference_with_yolo_val(yolo_inference_options)
        
        # Run again, with chunked inference and no symlinks
        
        inference_output_file_yolo_val_checkpoints_no_links = \
            insert_before_extension(inference_output_file_yolo_val,'checkpoints-no-links')
        yolo_inference_options.output_file = inference_output_file_yolo_val_checkpoints_no_links
        yolo_inference_options.use_symlinks = False
        yolo_inference_options.checkpoint_frequency = 5
        run_inference_with_yolo_val(yolo_inference_options)
    
        fn1 = inference_output_file_yolo_val
        
        output_files_to_compare = [
            inference_output_file_yolo_val_no_links,
            inference_output_file_yolo_val_checkpoints,
            inference_output_file_yolo_val_checkpoints_no_links
            ]
    
        for fn2 in output_files_to_compare:
            assert output_files_are_identical(fn1, fn2, verbose=True)
                            
    # ...if we need to run the YOLO val inference tests
    
    
    if not options.skip_video_tests:
        
        ## Video test (single video)
       
        print('\n** Running MD on a single video **\n')
        
        from megadetector.detection.process_video import ProcessVideoOptions, process_video
        from megadetector.utils.path_utils import insert_before_extension
        
        video_options = ProcessVideoOptions()
        video_options.model_file = options.default_model
        video_options.input_video_file = os.path.join(options.scratch_dir,options.test_videos[0])
        video_options.output_json_file = os.path.join(options.scratch_dir,'single_video_output.json')
        video_options.output_video_file = os.path.join(options.scratch_dir,'video_scratch/rendered_video.mp4')
        video_options.frame_folder = os.path.join(options.scratch_dir,'video_scratch/frame_folder')
        video_options.frame_rendering_folder = os.path.join(options.scratch_dir,'video_scratch/rendered_frame_folder')    
        video_options.render_output_video = True
        # video_options.keep_rendered_frames = False
        # video_options.keep_rendered_frames = False
        video_options.force_extracted_frame_folder_deletion = True
        video_options.force_rendered_frame_folder_deletion = True
        # video_options.reuse_results_if_available = False
        # video_options.reuse_frames_if_available = False
        video_options.recursive = True
        video_options.verbose = False
        video_options.fourcc = options.video_fourcc
        # video_options.rendering_confidence_threshold = None
        # video_options.json_confidence_threshold = 0.005
        video_options.frame_sample = 10
        video_options.n_cores = 5
        # video_options.debug_max_frames = -1
        # video_options.class_mapping_filename = None
        
        _ = process_video(video_options)
    
        assert os.path.isfile(video_options.output_video_file), \
            'Python video test failed to render output video file'
        assert os.path.isfile(video_options.output_json_file), \
            'Python video test failed to render output .json file'
            
        
        ## Video test (folder)
        
        print('\n** Running MD on a folder of videos **\n')
        
        from megadetector.detection.process_video import ProcessVideoOptions, process_video_folder
        from megadetector.utils.path_utils import insert_before_extension
        
        video_options = ProcessVideoOptions()
        video_options.model_file = options.default_model
        video_options.input_video_file = os.path.join(options.scratch_dir,
                                                      os.path.dirname(options.test_videos[0]))
        video_options.output_json_file = os.path.join(options.scratch_dir,'video_folder_output.json')
        video_options.output_video_file = None
        video_options.frame_folder = os.path.join(options.scratch_dir,'video_scratch/frame_folder')
        video_options.frame_rendering_folder = os.path.join(options.scratch_dir,'video_scratch/rendered_frame_folder')    
        video_options.render_output_video = False
        video_options.keep_rendered_frames = False
        video_options.keep_rendered_frames = False
        video_options.force_extracted_frame_folder_deletion = True
        video_options.force_rendered_frame_folder_deletion = True
        video_options.reuse_results_if_available = False
        video_options.reuse_frames_if_available = False
        video_options.recursive = True
        video_options.verbose = True
        video_options.fourcc = options.video_fourcc
        # video_options.rendering_confidence_threshold = None
        # video_options.json_confidence_threshold = 0.005
        video_options.frame_sample = 10  
        video_options.n_cores = 5        
        
        # Force frame extraction to disk, since that's how we generated our expected results file
        video_options.force_on_disk_frame_extraction = True
        # video_options.debug_max_frames = -1
        # video_options.class_mapping_filename = None
        
        # Use quality == None, because we can't control whether YOLOv5 has patched cm2.imread,
        # and therefore can't rely on using the quality parameter
        video_options.quality = None
        video_options.max_width = None
        
        _ = process_video_folder(video_options)
    
        assert os.path.isfile(video_options.output_json_file), \
            'Python video test failed to render output .json file'
            
        frame_output_file = insert_before_extension(video_options.output_json_file,'frames')
        assert os.path.isfile(frame_output_file)
        
        
        ## Verify results
        
        expected_results_file = \
            get_expected_results_filename(is_gpu_available(verbose=False),test_type='video',options=options)
        assert os.path.isfile(expected_results_file)
        compare_results(frame_output_file,expected_results_file,options)
        
        
        ## Run again, this time in memory, and make sure the results are *almost* the same
        
        # They won't be quite the same, because the on-disk path goes through a jpeg intermediate
        
        print('\n** Running MD on a folder of videos (in memory) **\n')
        
        video_options.output_json_file = insert_before_extension(video_options.output_json_file,'in-memory')
        video_options.force_on_disk_frame_extraction = False
        _ = process_video_folder(video_options)
                
        frame_output_file_in_memory = insert_before_extension(video_options.output_json_file,'frames')
        assert os.path.isfile(frame_output_file_in_memory)
        
        from copy import deepcopy
        options_loose = deepcopy(options)
        options_loose.max_conf_error = 0.05
        options_loose.max_coord_error = 0.01
        
        compare_results(inference_output_file=frame_output_file,
                        expected_results_file=frame_output_file_in_memory,
                        options=options_loose)
                
    # ...if we're not skipping video tests
    
    print('\n*** Finished module tests ***\n')

# ...def run_python_tests(...)


#%% Command-line tests

def run_cli_tests(options):
    """
    Runs CLI (as opposed to Python-based) package tests.
    
    Args:
        options (MDTestOptions): see MDTestOptions for details
    """
    
    print('\n*** Starting CLI tests ***\n')
    
    
    ## Environment management
    
    if options.cli_test_pythonpath is not None:
        os.environ['PYTHONPATH'] = options.cli_test_pythonpath 
        
        
    ## chdir if necessary
    
    if options.cli_working_dir is not None:
        os.chdir(options.cli_working_dir)
    
    
    ## Prepare data
    
    download_test_data(options)
    
    
    ## Run inference on an image
    
    image_fn = os.path.join(options.scratch_dir,options.test_images[0])
    output_dir = os.path.join(options.scratch_dir,'single_image_test')
    if options.cli_working_dir is None:
        cmd = 'python -m megadetector.detection.run_detector'
    else:
        cmd = 'python megadetector/detection/run_detector.py'
    cmd += ' "{}" --image_file "{}" --output_dir "{}"'.format(
        options.default_model,image_fn,output_dir)
    cmd_results = execute_and_print(cmd)
    
    if options.cpu_execution_is_error:
        gpu_available_via_cli = False
        for s in cmd_results['output']:
            if 'GPU available: True' in s:
                gpu_available_via_cli = True
                break
        if not gpu_available_via_cli:
            raise Exception('GPU execution is required, but not available')

    
    ## Run inference on a folder
    
    
    image_folder = os.path.join(options.scratch_dir,'md-test-images')
    assert os.path.isdir(image_folder), 'Test image folder {} is not available'.format(image_folder)
    inference_output_file = os.path.join(options.scratch_dir,'folder_inference_output.json')
    if options.cli_working_dir is None:
        cmd = 'python -m megadetector.detection.run_detector_batch'
    else:
        cmd = 'python megadetector/detection/run_detector_batch.py'
    cmd += ' "{}" "{}" "{}" --recursive'.format(
        options.default_model,image_folder,inference_output_file)
    cmd += ' --output_relative_filenames --quiet --include_image_size'
    cmd += ' --include_image_timestamp --include_exif_data'
    cmd_results = execute_and_print(cmd)
    
    base_cmd = cmd
    
    
    ## Run again with checkpointing enabled, make sure the results are the same
    
    from megadetector.utils.path_utils import insert_before_extension
        
    checkpoint_string = ' --checkpoint_frequency 5'
    cmd = base_cmd + checkpoint_string
    inference_output_file_checkpoint = insert_before_extension(inference_output_file,'_checkpoint')
    cmd = cmd.replace(inference_output_file,inference_output_file_checkpoint)
    cmd_results = execute_and_print(cmd)
    
    assert output_files_are_identical(fn1=inference_output_file, 
                                      fn2=inference_output_file_checkpoint,
                                      verbose=True)
    
    
    ## Run again with the image queue enabled, make sure the results are the same
    
    cmd = base_cmd + ' --use_image_queue'
    from megadetector.utils.path_utils import insert_before_extension
    inference_output_file_queue = insert_before_extension(inference_output_file,'_queue')
    cmd = cmd.replace(inference_output_file,inference_output_file_queue)
    cmd_results = execute_and_print(cmd)
    
    assert output_files_are_identical(fn1=inference_output_file, 
                                      fn2=inference_output_file_queue,
                                      verbose=True)
    
    
    ## Run again on multiple cores, make sure the results are the same
    
    # First run again on the CPU on a single thread if necessary, so we get a file that 
    # *should* be identical to the multicore version.
    
    gpu_available = is_gpu_available(verbose=False)
    
    cuda_visible_devices = None
    if 'CUDA_VISIBLE_DEVICES' in os.environ:
        cuda_visible_devices = os.environ['CUDA_VISIBLE_DEVICES']
    os.environ['CUDA_VISIBLE_DEVICES'] = '-1'    
    
    # If we already ran on the CPU, no need to run again
    if not gpu_available:
        inference_output_file_cpu = inference_output_file
    else:
        inference_output_file_cpu = insert_before_extension(inference_output_file,'cpu')    
        cmd = base_cmd
        cmd = cmd.replace(inference_output_file,inference_output_file_cpu)    
        cmd_results = execute_and_print(cmd)
        
    cpu_string = ' --ncores 4'
    cmd = base_cmd + cpu_string
    from megadetector.utils.path_utils import insert_before_extension
    inference_output_file_cpu_multicore = insert_before_extension(inference_output_file,'multicore')
    cmd = cmd.replace(inference_output_file,inference_output_file_cpu_multicore)
    cmd_results = execute_and_print(cmd)
    
    if cuda_visible_devices is not None:
        print('Restoring CUDA_VISIBLE_DEVICES')
        os.environ['CUDA_VISIBLE_DEVICES'] = cuda_visible_devices
    else:
        del os.environ['CUDA_VISIBLE_DEVICES']
        
    assert output_files_are_identical(fn1=inference_output_file_cpu, 
                                      fn2=inference_output_file_cpu_multicore,
                                      verbose=True)
    
    
    ## Postprocessing
    
    postprocessing_output_dir = os.path.join(options.scratch_dir,'postprocessing_output_cli')
    
    if options.cli_working_dir is None:
        cmd = 'python -m megadetector.postprocessing.postprocess_batch_results'
    else:
        cmd = 'python megadetector/postprocessing/postprocess_batch_results.py'
    cmd += ' "{}" "{}"'.format(
        inference_output_file,postprocessing_output_dir)
    cmd += ' --image_base_dir "{}"'.format(image_folder)
    cmd_results = execute_and_print(cmd)
                
    
    ## RDE
    
    rde_output_dir = os.path.join(options.scratch_dir,'rde_output_cli')
    
    if options.cli_working_dir is None:
        cmd = 'python -m megadetector.postprocessing.repeat_detection_elimination.find_repeat_detections'
    else:
        cmd = 'python  megadetector/postprocessing/repeat_detection_elimination/find_repeat_detections.py'
    cmd += ' "{}"'.format(inference_output_file)
    cmd += ' --imageBase "{}"'.format(image_folder)
    cmd += ' --outputBase "{}"'.format(rde_output_dir)
    cmd += ' --occurrenceThreshold 1' # Use an absurd number here to make sure we get some suspicious detections
    cmd_results = execute_and_print(cmd)    
    
    # Find the latest filtering folder
    filtering_output_dir = os.listdir(rde_output_dir)
    filtering_output_dir = [fn for fn in filtering_output_dir if fn.startswith('filtering_')]
    filtering_output_dir = [os.path.join(rde_output_dir,fn) for fn in filtering_output_dir]
    filtering_output_dir = [fn for fn in filtering_output_dir if os.path.isdir(fn)]
    filtering_output_dir = sorted(filtering_output_dir)[-1]
    
    print('Using RDE filtering folder {}'.format(filtering_output_dir))
    
    filtered_output_file = inference_output_file.replace('.json','_filtered.json')
    
    if options.cli_working_dir is None:
        cmd = 'python -m megadetector.postprocessing.repeat_detection_elimination.remove_repeat_detections'
    else:
        cmd = 'python  megadetector/postprocessing/repeat_detection_elimination/remove_repeat_detections.py'
    cmd += ' "{}" "{}" "{}"'.format(inference_output_file,filtered_output_file,filtering_output_dir)
    cmd_results = execute_and_print(cmd)
    
    assert os.path.isfile(filtered_output_file), \
        'Could not find RDE output file {}'.format(filtered_output_file)
    
    
    ## Run inference on a folder (tiled)
    
    image_folder = os.path.join(options.scratch_dir,'md-test-images')
    tiling_folder = os.path.join(options.scratch_dir,'tiling-folder')
    inference_output_file_tiled = os.path.join(options.scratch_dir,'folder_inference_output_tiled.json')
    if options.cli_working_dir is None:
        cmd = 'python -m megadetector.detection.run_tiled_inference'
    else:
        cmd = 'python megadetector/detection/run_tiled_inference.py'
    cmd += ' "{}" "{}" "{}" "{}"'.format(
        options.default_model,image_folder,tiling_folder,inference_output_file_tiled)
    cmd += ' --overwrite_handling overwrite'
    cmd_results = execute_and_print(cmd)
    
    with open(inference_output_file_tiled,'r') as f:
        results_from_file = json.load(f) # noqa
        
    
    ## Run inference on a folder (augmented, w/YOLOv5 val script)
    
    if options.yolo_working_dir is None:
        
        print('Bypassing YOLOv5 val tests, no yolo folder supplied')
        
    else:
    
        image_folder = os.path.join(options.scratch_dir,'md-test-images')
        yolo_results_folder = os.path.join(options.scratch_dir,'yolo-output-folder')
        yolo_symlink_folder = os.path.join(options.scratch_dir,'yolo-symlink_folder')
        inference_output_file_yolo_val = os.path.join(options.scratch_dir,'folder_inference_output_yolo_val.json')
        if options.cli_working_dir is None:
            cmd = 'python -m megadetector.detection.run_inference_with_yolov5_val'
        else:
            cmd = 'python megadetector/detection/run_inference_with_yolov5_val.py'
        cmd += ' "{}" "{}" "{}"'.format(
            options.default_model,image_folder,inference_output_file_yolo_val)
        cmd += ' --yolo_working_folder "{}"'.format(options.yolo_working_dir)
        cmd += ' --yolo_results_folder "{}"'.format(yolo_results_folder)
        cmd += ' --symlink_folder "{}"'.format(yolo_symlink_folder)
        cmd += ' --augment_enabled 1'
        # cmd += ' --no_use_symlinks'
        cmd += ' --overwrite_handling overwrite'
        cmd_results = execute_and_print(cmd)
        
        # Run again with checkpointing, make sure the output are identical
        cmd += ' --checkpoint_frequency 5'
        inference_output_file_yolo_val_checkpoint = \
            os.path.join(options.scratch_dir,'folder_inference_output_yolo_val_checkpoint.json')
        assert inference_output_file_yolo_val_checkpoint != inference_output_file_yolo_val
        cmd = cmd.replace(inference_output_file_yolo_val,inference_output_file_yolo_val_checkpoint)
        cmd_results = execute_and_print(cmd)
        
        assert output_files_are_identical(fn1=inference_output_file_yolo_val,
                                          fn2=inference_output_file_yolo_val_checkpoint,
                                          verbose=True)
        
    if not options.skip_video_tests:
            
        ## Video test
        
        video_inference_output_file = os.path.join(options.scratch_dir,'video_inference_output.json')
        output_video_file = os.path.join(options.scratch_dir,'video_scratch/cli_rendered_video.mp4')
        frame_folder = os.path.join(options.scratch_dir,'video_scratch/frame_folder_cli')
        frame_rendering_folder = os.path.join(options.scratch_dir,'video_scratch/rendered_frame_folder_cli')        
        
        video_fn = os.path.join(options.scratch_dir,options.test_videos[-1])        
        assert os.path.isfile(video_fn), 'Could not find video file {}'.format(video_fn)
        
        output_dir = os.path.join(options.scratch_dir,'single_video_test_cli')
        if options.cli_working_dir is None:
            cmd = 'python -m megadetector.detection.process_video'
        else:
            cmd = 'python megadetector/detection/process_video.py'
        cmd += ' "{}" "{}"'.format(options.default_model,video_fn)
        cmd += ' --frame_folder "{}" --frame_rendering_folder "{}" --output_json_file "{}" --output_video_file "{}"'.format(
            frame_folder,frame_rendering_folder,video_inference_output_file,output_video_file)
        cmd += ' --render_output_video --fourcc {}'.format(options.video_fourcc)
        cmd += ' --force_extracted_frame_folder_deletion --force_rendered_frame_folder_deletion --n_cores 5 --frame_sample 3'
        cmd += ' --verbose'
        cmd_results = execute_and_print(cmd)

    # ...if we're not skipping video tests
    
    
    ## Run inference on a folder (with MDV5B, so we can do a comparison)
    
    image_folder = os.path.join(options.scratch_dir,'md-test-images')
    inference_output_file_alt = os.path.join(options.scratch_dir,'folder_inference_output_alt.json')
    if options.cli_working_dir is None:
        cmd = 'python -m megadetector.detection.run_detector_batch'
    else:
        cmd = 'python megadetector/detection/run_detector_batch.py'
    cmd += ' "{}" "{}" "{}" --recursive'.format(
        options.alt_model,image_folder,inference_output_file_alt)
    cmd += ' --output_relative_filenames --quiet --include_image_size'
    cmd += ' --include_image_timestamp --include_exif_data'
    cmd_results = execute_and_print(cmd)
    
    with open(inference_output_file_alt,'r') as f:
        results_from_file = json.load(f) # noqa
    
    
    ## Compare the two files
    
    comparison_output_folder = os.path.join(options.scratch_dir,'results_comparison')
    image_folder = os.path.join(options.scratch_dir,'md-test-images')
    results_files_string = '"{}" "{}"'.format(
        inference_output_file,inference_output_file_alt)
    if options.cli_working_dir is None:
        cmd = 'python -m megadetector.postprocessing.compare_batch_results'
    else:
        cmd = 'python megadetector/postprocessing/compare_batch_results.py'
    cmd += ' "{}" "{}" {}'.format(comparison_output_folder,image_folder,results_files_string)
    cmd_results = execute_and_print(cmd)
    
    assert cmd_results['status'] == 0, 'Error generating comparison HTML'
    assert os.path.isfile(os.path.join(comparison_output_folder,'index.html')), \
        'Failed to generate comparison HTML'
    
    print('\n*** Finished CLI tests ***\n')
    
# ...def run_cli_tests(...)


#%% Main test wrapper

def run_tests(options):
    """
    Runs Python-based and/or CLI-based package tests.
    
    Args:
        options (MDTestOptions): see MDTestOptions for details
    """
    
    # Prepare data folder
    download_test_data(options)
    
    if options.disable_gpu:
        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
        
    # Verify GPU
    gpu_available = is_gpu_available()
    
    # If the GPU is required and isn't available, error
    if options.cpu_execution_is_error and (not gpu_available):
        raise ValueError('GPU not available, and cpu_execution_is_error is set')
    
    # If the GPU should be disabled, verify that it is
    if options.disable_gpu:
        assert (not gpu_available), 'CPU execution specified, but the GPU appears to be available'
        
    # Run python tests
    if not options.skip_python_tests:
        run_python_tests(options)
    
    # Run CLI tests
    if not options.skip_cli_tests:
        run_cli_tests(options)


#%% Interactive driver

if False:
    
    pass

    #%%
    
    options = MDTestOptions()
    
    options.disable_gpu = False
    options.cpu_execution_is_error = False
    options.skip_video_tests = False
    options.skip_python_tests = False
    options.skip_cli_tests = True
    options.scratch_dir = None
    options.test_data_url = 'https://lila.science/public/md-test-package.zip'
    options.force_data_download = False
    options.force_data_unzip = False
    options.warning_mode = False
    options.max_coord_error = 0.001
    options.max_conf_error = 0.005
    options.cli_working_dir = r'c:\git\MegaDetector'
    options.yolo_working_dir = r'c:\git\yolov5-md'

    
    #%%
    
    import os
    if 'PYTHONPATH' not in os.environ or options.yolo_working_dir not in os.environ['PYTHONPATH']:
        os.environ['PYTHONPATH'] += ';' + options.yolo_working_dir

    #%%
    
    run_tests(options)
    
    
#%% Command-line driver

def main():

    options = MDTestOptions()
    
    parser = argparse.ArgumentParser(
        description='MegaDetector test suite')
    
    parser.add_argument(
        '--disable_gpu',
        action='store_true',
        help='Disable GPU operation')
    
    parser.add_argument(
        '--cpu_execution_is_error',
        action='store_true',
        help='Fail if the GPU appears not to be available')
    
    parser.add_argument(
        '--scratch_dir',
        default=None,
        type=str,
        help='Directory for temporary storage (defaults to system temp dir)')
    
    parser.add_argument(
        '--skip_video_tests',
        action='store_true',
        help='Skip tests related to video (which can be slow)')
        
    parser.add_argument(
        '--skip_python_tests',
        action='store_true',
        help='Skip python tests')
        
    parser.add_argument(
        '--skip_cli_tests',
        action='store_true',
        help='Skip CLI tests')
        
    parser.add_argument(
        '--force_data_download',
        action='store_true',
        help='Force download of the test data file, even if it\'s already available')
    
    parser.add_argument(
        '--force_data_unzip',
        action='store_true',
        help='Force extraction of all files in the test data file, even if they\'re already available')
    
    parser.add_argument(
        '--warning_mode',
        action='store_true',
        help='Turns numeric/content errors into warnings')
    
    parser.add_argument(
        '--max_conf_error',
        type=float,
        default=options.max_conf_error,
        help='Maximum tolerable confidence value deviation from expected (default {})'.format(
            options.max_conf_error))
    
    parser.add_argument(
        '--max_coord_error',
        type=float,
        default=options.max_coord_error,
        help='Maximum tolerable coordinate value deviation from expected (default {})'.format(
            options.max_coord_error))

    parser.add_argument(
        '--cli_working_dir',
        type=str,
        default=None,
        help='Working directory for CLI tests')
    
    parser.add_argument(
        '--yolo_working_dir',
        type=str,
        default=None,
        help='Working directory for yolo inference tests')

    parser.add_argument(
        '--cli_test_pythonpath',
        type=str,
        default=None,
        help='PYTHONPATH to set for CLI tests; if None, inherits from the parent process'
        )
    
    # token used for linting
    #
    # no_arguments_required
        
    args = parser.parse_args()
    
    _args_to_object(args,options)
    
    run_tests(options)
    
if __name__ == '__main__':    
    main()


#%% Sample invocations

r"""
# Windows
set PYTHONPATH=c:\git\MegaDetector;c:\git\yolov5-md
cd c:\git\MegaDetector\megadetector\utils
python md_tests.py --cli_working_dir "c:\git\MegaDetector" --yolo_working_dir "c:\git\yolov5-md" --cli_test_pythonpath "c:\git\MegaDetector;c:\git\yolov5-md"

# Linux
export PYTHONPATH=/mnt/c/git/MegaDetector:/mnt/c/git/yolov5-md
cd /mnt/c/git/MegaDetector/megadetector/utils
python md_tests.py --cli_working_dir "/mnt/c/git/MegaDetector" --yolo_working_dir "/mnt/c/git/yolov5-md" --cli_test_pythonpath "/mnt/c/git/MegaDetector:/mnt/c/git/yolov5-md"

python -c "import md_tests; print(md_tests.get_expected_results_filename(True))"
"""


#%% Scrap

if False:

    pass

    #%%
    
    import sys; sys.path.append(r'c:\git\yolov5-md')
    
    #%%
    
    fn1 = r"G:\temp\md-test-package\mdv5a-video-cpu-pt1.10.1.frames.json"
    fn2 = r"G:\temp\md-test-package\mdv5a-video-gpu-pt1.10.1.frames.json"
    fn3 = r"G:\temp\md-test-package\mdv5a-video-cpu-pt2.x.frames.json"
    fn4 = r"G:\temp\md-test-package\mdv5a-video-gpu-pt2.x.frames.json"
    
    assert all([os.path.isfile(fn) for fn in [fn1,fn2,fn3,fn4]])
    print(output_files_are_identical(fn1,fn1,verbose=False))
    print(output_files_are_identical(fn1,fn2,verbose=False))
    print(output_files_are_identical(fn1,fn3,verbose=False))
    
    #%%
    
    fn1 = r"G:\temp\md-test-package\mdv5a-image-gpu-pt1.10.1.json"
    fn2 = r"G:\temp\md-test-package\mdv5a-augment-image-gpu-pt1.10.1.json"
    print(output_files_are_identical(fn1,fn2,verbose=True))
    
    fn1 = r"G:\temp\md-test-package\mdv5a-image-cpu-pt1.10.1.json"
    fn2 = r"G:\temp\md-test-package\mdv5a-augment-image-cpu-pt1.10.1.json"
    print(output_files_are_identical(fn1,fn2,verbose=True))
